docs: def p2x(self, p):
          """ Map parameters ``p`` to vector in x-space.
  
          x-space is a vector space of dimension ``p.size``. Its axes are
          in the directions specified by the eigenvectors of ``p``'s covariance
          matrix, and distance along an axis is in units of the standard
          deviation in that direction.
          """
          if hasattr(p, 'keys'):
              dp = BufferDict(p, keys=self.g.keys())._buf[:self.meanflat.size] - self.meanflat
          else:
              dp = numpy.asarray(p).reshape(-1) - self.meanflat
          return self.vec_isig.dot(dp)